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Managing technology performance risk in the strategic design of biomass-based supply chains for energy in the transport sector


  • d'Amore, Federico
  • Bezzo, Fabrizio


Biomass has long been considered one of the most promising feedstock as an alternative primary source to substitute traditional fuels in the transport sectors. However, both biomass intrinsic variability and the fact that several conversion technologies have not reached full maturity make the economic assessment of the production system performance rather difficult. This paper proposes a quantitative approach for the strategic design and optimisation of biomass-based supply chains under uncertainty on technology conversion efficiency. The methodology is based on regret theory and allows quantifying both risk and regret with respect to benchmark economic outputs. A Mixed Integer Linear Programming is employed to represent and optimise the profitability of a multi-echelon, multi-period and spatially explicit biomass-based supply chain for bioethanol and bioelectricity production where several conversion technologies are simultaneously taken into account. The modelling framework includes biomass cultivation, transport, conversion, distribution and final usage in alternative fuel vehicles (running either on bioethanol or bioelectricity). Results demonstrate how the methodology can help policy-makers and investors assessing technological options according to their risk aversion attitude.

Suggested Citation

  • d'Amore, Federico & Bezzo, Fabrizio, 2017. "Managing technology performance risk in the strategic design of biomass-based supply chains for energy in the transport sector," Energy, Elsevier, vol. 138(C), pages 563-574.
  • Handle: RePEc:eee:energy:v:138:y:2017:i:c:p:563-574
    DOI: 10.1016/

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    References listed on IDEAS

    1. Weiss, Martin & Patel, Martin K. & Junginger, Martin & Perujo, Adolfo & Bonnel, Pierre & van Grootveld, Geert, 2012. "On the electrification of road transport - Learning rates and price forecasts for hybrid-electric and battery-electric vehicles," Energy Policy, Elsevier, vol. 48(C), pages 374-393.
    2. Arnold, Uwe & Yildiz, Özgür, 2015. "Economic risk analysis of decentralized renewable energy infrastructures – A Monte Carlo Simulation approach," Renewable Energy, Elsevier, vol. 77(C), pages 227-239.
    3. Ahmed, Sajjad & Elsholkami, Mohamed & Elkamel, Ali & Du, Juan & Ydstie, Erik B. & Douglas, Peter L., 2014. "Financial risk management for new technology integration in energy planning under uncertainty," Applied Energy, Elsevier, vol. 128(C), pages 75-81.
    4. Loomes, Graham & Sugden, Robert, 1982. "Regret Theory: An Alternative Theory of Rational Choice under Uncertainty," Economic Journal, Royal Economic Society, vol. 92(368), pages 805-824, December.
    5. repec:eee:joecas:v:12:y:2015:i:2:p:142-152 is not listed on IDEAS
    6. Nooraie, S. Vahid & Mellat Parast, Mahour, 2015. "A multi-objective approach to supply chain risk management: Integrating visibility with supply and demand risk," International Journal of Production Economics, Elsevier, vol. 161(C), pages 192-200.
    7. Han Bleichrodt & Peter P. Wakker, 2015. "Regret Theory: A Bold Alternative to the Alternatives," Economic Journal, Royal Economic Society, vol. 0(583), pages 493-532, March.
    8. Khatiwada, Dilip & Leduc, Sylvain & Silveira, Semida & McCallum, Ian, 2016. "Optimizing ethanol and bioelectricity production in sugarcane biorefineries in Brazil," Renewable Energy, Elsevier, vol. 85(C), pages 371-386.
    9. Bartunek, Kenneth S & Chowdhury, Mustafa, 1997. "Implied Risk Aversion Parameter from Option Prices," The Financial Review, Eastern Finance Association, vol. 32(1), pages 107-124, February.
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